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     "output_type": "stream",
     "text": [
      "2024-05-12 10:08:48.973849: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-05-12 10:08:48.973973: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-05-12 10:08:49.101183: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start on cuda:0 device.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import torch\n",
    "import torchvision\n",
    "from PIL import Image\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torchvision import models, transforms\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(\"start on {} device.\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f6704ab",
   "metadata": {
    "execution": {
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   "source": [
    "# 保存训练模型名称\n",
    "save_model_name = \"vgg16_base_cifar10\"\n",
    "# 训练的轮数\n",
    "epoch = 50\n",
    "# 一批数据大小\n",
    "batch_size = 128\n",
    "# 丢弃率\n",
    "dropout = 0.5\n",
    "# 数据集路径\n",
    "# /kaggle/input/dogs-vs-cats\n",
    "dataset_path = \"./dataset\"\n",
    "# tensorboard路径\n",
    "writer = SummaryWriter(\"./logs\")\n",
    "# 学习率\n",
    "# learning_rate = 0.01\n",
    "# 1e-2=1 x (10)^(-2) = 1 /100 = 0.01\n",
    "learning_rate = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0234630",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./dataset/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 170498071/170498071 [00:04<00:00, 34342082.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./dataset/cifar-10-python.tar.gz to ./dataset\n",
      "Files already downloaded and verified\n",
      "训练数据集长度: 50000\n",
      "测试数据集长度: 10000\n",
      "torch.Size([128, 3, 32, 32])\n",
      "torch.Size([128, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.ToTensor()\n",
    "train_data = torchvision.datasets.CIFAR10(dataset_path, train=True, download=True,\n",
    "                                          transform=transform)\n",
    "train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=False)\n",
    "\n",
    "test_data = torchvision.datasets.CIFAR10(dataset_path, train=False, download=True,\n",
    "                                         transform=transform)\n",
    "test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=False)\n",
    "\n",
    "train_data_size = len(train_data)\n",
    "test_data_size = len(test_data)\n",
    "print(\"训练数据集长度: {}\".format(train_data_size))\n",
    "print(\"测试数据集长度: {}\".format(test_data_size))\n",
    "\n",
    "for data in train_dataloader:\n",
    "    imgs, targets = data\n",
    "    print(imgs.shape)\n",
    "    break\n",
    "\n",
    "for data in test_dataloader:\n",
    "    imgs, targets = data\n",
    "    print(imgs.shape)\n",
    "    break"
   ]
  },
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   "source": [
    "class VGG16_BASE(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(VGG16_BASE, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            # vgg16原本时 nn.AdaptiveAvgPool2d((7, 7))，由于输入图片大小导致\n",
    "            # nn.AvgPool2d(kernel_size=1, stride=1)\n",
    "        )\n",
    "        self.classifier = nn.Sequential(\n",
    "            # 原始模型vgg16输入image大小是224 x 224\n",
    "            # CIFAR10输入image大小是32 x 32\n",
    "            # 大小是小了 7 x 7倍\n",
    "            nn.Linear(512, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 1000),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(1000, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = self.features(x)\n",
    "        # output = output.view(output.size(0), -1)\n",
    "        output = torch.flatten(output, 1)\n",
    "        output = self.classifier(output)\n",
    "        return output"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG16_BASE(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (12): ReLU(inplace=True)\n",
      "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (16): ReLU(inplace=True)\n",
      "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (19): ReLU(inplace=True)\n",
      "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (26): ReLU(inplace=True)\n",
      "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (32): ReLU(inplace=True)\n",
      "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (36): ReLU(inplace=True)\n",
      "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (39): ReLU(inplace=True)\n",
      "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (42): ReLU(inplace=True)\n",
      "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=512, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Dropout(p=0.5, inplace=False)\n",
      "    (9): Linear(in_features=1000, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 网络模型相关初始化\n",
    "# 创建网络模型\n",
    "mymod = VGG16_BASE()\n",
    "print(mymod)\n",
    "\n",
    "mymod = mymod.to(device)\n",
    "# 损失函数\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "# loss_fn = loss_fn.to(device)\n",
    "\n",
    "# 优化器\n",
    "optimizer = torch.optim.SGD(mymod.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
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    {
     "name": "stdout",
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     "text": [
      "start train.\n",
      "第0轮训练用时：15.159218549728394 s\n",
      "第0轮训练loss：1.876008526687622\n",
      "第0轮训练精度：0.27246\n",
      "------\n",
      "第0轮测试用时：2.350301742553711 s\n",
      "第0轮测试loss：1.5083186700820923\n",
      "第0轮测试精度：0.4305\n",
      "------\n",
      "第1轮训练用时：14.250172138214111 s\n",
      "第1轮训练loss：1.2687045865631104\n",
      "第1轮训练精度：0.5307\n",
      "------\n",
      "第1轮测试用时：2.31242036819458 s\n",
      "第1轮测试loss：1.3153383003234864\n",
      "第1轮测试精度：0.5606\n",
      "------\n",
      "第2轮训练用时：14.167919635772705 s\n",
      "第2轮训练loss：0.9421814789962768\n",
      "第2轮训练精度：0.66018\n",
      "------\n",
      "第2轮测试用时：2.366326093673706 s\n",
      "第2轮测试loss：1.281828320121765\n",
      "第2轮测试精度：0.5868\n",
      "------\n",
      "第3轮训练用时：14.185431003570557 s\n",
      "第3轮训练loss：0.731765878314972\n",
      "第3轮训练精度：0.74188\n",
      "------\n",
      "第3轮测试用时：2.3003716468811035 s\n",
      "第3轮测试loss：1.0570671388626098\n",
      "第3轮测试精度：0.6421\n",
      "------\n",
      "第4轮训练用时：14.195542573928833 s\n",
      "第4轮训练loss：0.5837407637023926\n",
      "第4轮训练精度：0.79676\n",
      "------\n",
      "第4轮测试用时：2.333714723587036 s\n",
      "第4轮测试loss：0.8593671525001526\n",
      "第4轮测试精度：0.7202\n",
      "------\n",
      "第5轮训练用时：14.196042537689209 s\n",
      "第5轮训练loss：0.45448941275596616\n",
      "第5轮训练精度：0.84264\n",
      "------\n",
      "第5轮测试用时：2.3603265285491943 s\n",
      "第5轮测试loss：1.0471660324096679\n",
      "第5轮测试精度：0.6971\n",
      "------\n",
      "第6轮训练用时：14.199694395065308 s\n",
      "第6轮训练loss：0.36073570773124697\n",
      "第6轮训练精度：0.87552\n",
      "------\n",
      "第6轮测试用时：2.2948617935180664 s\n",
      "第6轮测试loss：0.9601507865905762\n",
      "第6轮测试精度：0.7195\n",
      "------\n",
      "第7轮训练用时：14.189245700836182 s\n",
      "第7轮训练loss：0.27370866032600405\n",
      "第7轮训练精度：0.90546\n",
      "------\n",
      "第7轮测试用时：2.303524971008301 s\n",
      "第7轮测试loss：0.9383191900253296\n",
      "第7轮测试精度：0.7352\n",
      "------\n",
      "第8轮训练用时：14.203824043273926 s\n",
      "第8轮训练loss：0.2218505466365814\n",
      "第8轮训练精度：0.92498\n",
      "------\n",
      "第8轮测试用时：2.3187901973724365 s\n",
      "第8轮测试loss：1.1638422369003296\n",
      "第8轮测试精度：0.7109\n",
      "------\n",
      "第9轮训练用时：14.16547966003418 s\n",
      "第9轮训练loss：0.17179661837100982\n",
      "第9轮训练精度：0.9425\n",
      "------\n",
      "第9轮测试用时：2.306337833404541 s\n",
      "第9轮测试loss：1.0311678688049317\n",
      "第9轮测试精度：0.7406\n",
      "------\n",
      "第10轮训练用时：14.387364387512207 s\n",
      "第10轮训练loss：0.13820746173858642\n",
      "第10轮训练精度：0.9528\n",
      "------\n",
      "第10轮测试用时：2.30704927444458 s\n",
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      "------\n",
      "第11轮训练用时：14.208185195922852 s\n",
      "第11轮训练loss：0.11325377818465233\n",
      "第11轮训练精度：0.96142\n",
      "------\n",
      "第11轮测试用时：2.422236442565918 s\n",
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      "第11轮测试精度：0.7435\n",
      "------\n",
      "第12轮训练用时：14.228503942489624 s\n",
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      "第12轮训练精度：0.96932\n",
      "------\n",
      "第12轮测试用时：2.2658026218414307 s\n",
      "第12轮测试loss：1.2818838376998902\n",
      "第12轮测试精度：0.7259\n",
      "------\n",
      "第13轮训练用时：14.190977334976196 s\n",
      "第13轮训练loss：0.08810787591457367\n",
      "第13轮训练精度：0.97012\n",
      "------\n",
      "第13轮测试用时：2.2894482612609863 s\n",
      "第13轮测试loss：0.9684097909927368\n",
      "第13轮测试精度：0.7816\n",
      "------\n",
      "第14轮训练用时：14.200870037078857 s\n",
      "第14轮训练loss：0.059463455913066864\n",
      "第14轮训练精度：0.97982\n",
      "------\n",
      "第14轮测试用时：2.3078150749206543 s\n",
      "第14轮测试loss：1.1882259798049928\n",
      "第14轮测试精度：0.7624\n",
      "------\n",
      "第15轮训练用时：14.254657506942749 s\n",
      "第15轮训练loss：0.05149539193749428\n",
      "第15轮训练精度：0.98318\n",
      "------\n",
      "第15轮测试用时：2.3320775032043457 s\n",
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      "------\n",
      "第16轮训练用时：14.266295671463013 s\n",
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      "第16轮训练精度：0.98354\n",
      "------\n",
      "第16轮测试用时：2.3376047611236572 s\n",
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      "------\n",
      "第17轮训练用时：14.180465698242188 s\n",
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      "------\n",
      "第17轮测试用时：2.3098552227020264 s\n",
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      "------\n",
      "第18轮训练用时：14.207438468933105 s\n",
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      "第18轮训练精度：0.98844\n",
      "------\n",
      "第18轮测试用时：2.3324472904205322 s\n",
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      "------\n",
      "第19轮训练用时：14.27752947807312 s\n",
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      "第19轮训练精度：0.988\n",
      "------\n",
      "第19轮测试用时：2.4843759536743164 s\n",
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      "------\n",
      "第20轮训练用时：14.211062908172607 s\n",
      "第20轮训练loss：0.037908008180856705\n",
      "第20轮训练精度：0.98764\n",
      "------\n",
      "第20轮测试用时：2.332720994949341 s\n",
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      "------\n",
      "第21轮训练用时：14.280474662780762 s\n",
      "第21轮训练loss：0.035827886170744894\n",
      "第21轮训练精度：0.98828\n",
      "------\n",
      "第21轮测试用时：2.2967538833618164 s\n",
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      "------\n",
      "第22轮训练用时：14.208239316940308 s\n",
      "第22轮训练loss：0.02566504783719778\n",
      "第22轮训练精度：0.99154\n",
      "------\n",
      "第22轮测试用时：2.298151731491089 s\n",
      "第22轮测试loss：1.2150658363342286\n",
      "第22轮测试精度：0.7829\n",
      "------\n",
      "第23轮训练用时：14.23094654083252 s\n",
      "第23轮训练loss：0.02181185465529561\n",
      "第23轮训练精度：0.9932\n",
      "------\n",
      "第23轮测试用时：2.3151168823242188 s\n",
      "第23轮测试loss：1.1883511552810668\n",
      "第23轮测试精度：0.7887\n",
      "------\n",
      "第24轮训练用时：14.25499415397644 s\n",
      "第24轮训练loss：0.021259153535962104\n",
      "第24轮训练精度：0.99342\n",
      "------\n",
      "第24轮测试用时：2.363248109817505 s\n",
      "第24轮测试loss：1.1938773120880126\n",
      "第24轮测试精度：0.7915\n",
      "------\n",
      "第25轮训练用时：14.286870956420898 s\n",
      "第25轮训练loss：0.03051570731371641\n",
      "第25轮训练精度：0.98968\n",
      "------\n",
      "第25轮测试用时：2.348489284515381 s\n",
      "第25轮测试loss：1.0842762557983399\n",
      "第25轮测试精度：0.799\n",
      "------\n",
      "第26轮训练用时：14.228787183761597 s\n",
      "第26轮训练loss：0.020421721588671206\n",
      "第26轮训练精度：0.99352\n",
      "------\n",
      "第26轮测试用时：2.308769464492798 s\n",
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      "第26轮测试精度：0.7885\n",
      "------\n",
      "第27轮训练用时：14.236367225646973 s\n",
      "第27轮训练loss：0.01001972562983632\n",
      "第27轮训练精度：0.9967\n",
      "------\n",
      "第27轮测试用时：2.2931389808654785 s\n",
      "第27轮测试loss：1.1317261911392211\n",
      "第27轮测试精度：0.7994\n",
      "------\n",
      "第28轮训练用时：14.164562463760376 s\n",
      "第28轮训练loss：0.010385839332565665\n",
      "第28轮训练精度：0.9966\n",
      "------\n",
      "第28轮测试用时：2.3176910877227783 s\n",
      "第28轮测试loss：1.1009985408782959\n",
      "第28轮测试精度：0.8035\n",
      "------\n",
      "第29轮训练用时：14.185435056686401 s\n",
      "第29轮训练loss：0.013311283947601914\n",
      "第29轮训练精度：0.99592\n",
      "------\n",
      "第29轮测试用时：2.3344054222106934 s\n",
      "第29轮测试loss：1.2008168205261232\n",
      "第29轮测试精度：0.7951\n",
      "------\n",
      "第30轮训练用时：14.186889410018921 s\n",
      "第30轮训练loss：0.013553882884979248\n",
      "第30轮训练精度：0.9957\n",
      "------\n",
      "第30轮测试用时：2.276334524154663 s\n",
      "第30轮测试loss：1.1639515804290772\n",
      "第30轮测试精度：0.8058\n",
      "------\n",
      "第31轮训练用时：14.185457944869995 s\n",
      "第31轮训练loss：0.020866536430194975\n",
      "第31轮训练精度：0.99322\n",
      "------\n",
      "第31轮测试用时：2.307736873626709 s\n",
      "第31轮测试loss：1.2442915418624878\n",
      "第31轮测试精度：0.7874\n",
      "------\n",
      "第32轮训练用时：14.160074949264526 s\n",
      "第32轮训练loss：0.014030850895047188\n",
      "第32轮训练精度：0.99524\n",
      "------\n",
      "第32轮测试用时：2.271073579788208 s\n",
      "第32轮测试loss：1.1790177947998046\n",
      "第32轮测试精度：0.7985\n",
      "------\n",
      "第33轮训练用时：14.192366361618042 s\n",
      "第33轮训练loss：0.014072822620049118\n",
      "第33轮训练精度：0.99542\n",
      "------\n",
      "第33轮测试用时：2.2980306148529053 s\n",
      "第33轮测试loss：1.263910747718811\n",
      "第33轮测试精度：0.7873\n",
      "------\n",
      "第34轮训练用时：14.165719032287598 s\n",
      "第34轮训练loss：0.01302942057248205\n",
      "第34轮训练精度：0.99584\n",
      "------\n",
      "第34轮测试用时：2.373269557952881 s\n",
      "第34轮测试loss：1.1799196125030518\n",
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      "------\n",
      "第35轮训练用时：14.166118860244751 s\n",
      "第35轮训练loss：0.015695633888840675\n",
      "第35轮训练精度：0.9951\n",
      "------\n",
      "第35轮测试用时：2.313852548599243 s\n",
      "第35轮测试loss：1.1988835823059083\n",
      "第35轮测试精度：0.7955\n",
      "------\n",
      "第36轮训练用时：14.18515419960022 s\n",
      "第36轮训练loss：0.010872058566361666\n",
      "第36轮训练精度：0.99658\n",
      "------\n",
      "第36轮测试用时：2.3340466022491455 s\n",
      "第36轮测试loss：1.120074997329712\n",
      "第36轮测试精度：0.8091\n",
      "------\n",
      "第37轮训练用时：14.186639785766602 s\n",
      "第37轮训练loss：0.005479485749527812\n",
      "第37轮训练精度：0.99848\n",
      "------\n",
      "第37轮测试用时：2.329921245574951 s\n",
      "第37轮测试loss：1.1531020793914795\n",
      "第37轮测试精度：0.8072\n",
      "------\n",
      "第38轮训练用时：14.189898252487183 s\n",
      "第38轮训练loss：0.008075011825449764\n",
      "第38轮训练精度：0.99762\n",
      "------\n",
      "第38轮测试用时：2.292292594909668 s\n",
      "第38轮测试loss：1.2723115245819092\n",
      "第38轮测试精度：0.7941\n",
      "------\n",
      "第39轮训练用时：14.183084964752197 s\n",
      "第39轮训练loss：0.015083681808114051\n",
      "第39轮训练精度：0.99486\n",
      "------\n",
      "第39轮测试用时：2.2998530864715576 s\n",
      "第39轮测试loss：1.742316928100586\n",
      "第39轮测试精度：0.7446\n",
      "------\n",
      "第40轮训练用时：14.186361074447632 s\n",
      "第40轮训练loss：0.015323684927597642\n",
      "第40轮训练精度：0.99522\n",
      "------\n",
      "第40轮测试用时：2.299628973007202 s\n",
      "第40轮测试loss：1.4768868816375733\n",
      "第40轮测试精度：0.7773\n",
      "------\n",
      "第41轮训练用时：14.209595680236816 s\n",
      "第41轮训练loss：0.016240314466804268\n",
      "第41轮训练精度：0.99472\n",
      "------\n",
      "第41轮测试用时：2.2867398262023926 s\n",
      "第41轮测试loss：1.1728934036254883\n",
      "第41轮测试精度：0.8045\n",
      "------\n",
      "第42轮训练用时：14.18238878250122 s\n",
      "第42轮训练loss：0.01645498376622796\n",
      "第42轮训练精度：0.99446\n",
      "------\n",
      "第42轮测试用时：2.2856664657592773 s\n",
      "第42轮测试loss：1.2117595481872558\n",
      "第42轮测试精度：0.7954\n",
      "------\n",
      "第43轮训练用时：14.153219938278198 s\n",
      "第43轮训练loss：0.012246398115493357\n",
      "第43轮训练精度：0.99616\n",
      "------\n",
      "第43轮测试用时：2.290794610977173 s\n",
      "第43轮测试loss：1.3080463851928712\n",
      "第43轮测试精度：0.7798\n",
      "------\n",
      "第44轮训练用时：14.186748266220093 s\n",
      "第44轮训练loss：0.010151161339730024\n",
      "第44轮训练精度：0.99682\n",
      "------\n",
      "第44轮测试用时：2.3085925579071045 s\n",
      "第44轮测试loss：1.139734935951233\n",
      "第44轮测试精度：0.8065\n",
      "------\n",
      "第45轮训练用时：14.193734884262085 s\n",
      "第45轮训练loss：0.009715542831458151\n",
      "第45轮训练精度：0.99698\n",
      "------\n",
      "第45轮测试用时：2.3164358139038086 s\n",
      "第45轮测试loss：1.1354698154449463\n",
      "第45轮测试精度：0.8107\n",
      "------\n",
      "第46轮训练用时：14.173287391662598 s\n",
      "第46轮训练loss：0.004277753106653691\n",
      "第46轮训练精度：0.99874\n",
      "------\n",
      "第46轮测试用时：2.317984104156494 s\n",
      "第46轮测试loss：1.1375053256988525\n",
      "第46轮测试精度：0.8083\n",
      "------\n",
      "第47轮训练用时：14.166881561279297 s\n",
      "第47轮训练loss：0.0014727572960220277\n",
      "第47轮训练精度：0.9997\n",
      "------\n",
      "第47轮测试用时：2.287257671356201 s\n",
      "第47轮测试loss：1.10436164188385\n",
      "第47轮测试精度：0.8182\n",
      "------\n",
      "第48轮训练用时：14.211125135421753 s\n",
      "第48轮训练loss：0.0006386311685887631\n",
      "第48轮训练精度：0.99992\n",
      "------\n",
      "第48轮测试用时：2.3319950103759766 s\n",
      "第48轮测试loss：1.1048517868041992\n",
      "第48轮测试精度：0.8189\n",
      "------\n",
      "第49轮训练用时：14.18056035041809 s\n",
      "第49轮训练loss：0.0007313161471113563\n",
      "第49轮训练精度：0.99984\n",
      "------\n",
      "第49轮测试用时：2.29061222076416 s\n",
      "第49轮测试loss：1.1149946437835694\n",
      "第49轮测试精度：0.815\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "# 保存训练数据绘图\n",
    "train_loss_list = []\n",
    "train_accuracy_list = []\n",
    "\n",
    "test_loss_list = []\n",
    "test_accuracy_list = []\n",
    "\n",
    "epoch_step = []\n",
    "print(\"start train.\")\n",
    "for i in range(epoch):\n",
    "    epoch_step.append(i)\n",
    "    \n",
    "    mymod.train()\n",
    "    start_time = time.time()\n",
    "    total_train_loss_pre_epoch = 0\n",
    "    total_train_accuracy_pre_epoch = 0\n",
    "    for data in train_dataloader:\n",
    "        imgs, targets = data\n",
    "        # print(imgs.shape)\n",
    "        imgs = imgs.to(device)\n",
    "        targets = targets.to(device)\n",
    "        outputs = mymod(imgs)\n",
    "        accuracy = (outputs.argmax(1) == targets).sum()\n",
    "        total_train_accuracy_pre_epoch += accuracy.item()\n",
    "\n",
    "        loss = loss_fn(outputs, targets)\n",
    "        total_train_loss_pre_epoch += loss.item() * targets.size(0)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    end_train_time = time.time()\n",
    "    print(\"第{}轮训练用时：{} s\".format(i, end_train_time - start_time))\n",
    "    print(\"第{}轮训练loss：{}\".format(i, total_train_loss_pre_epoch / train_data_size))\n",
    "    print(\"第{}轮训练精度：{}\".format(i, total_train_accuracy_pre_epoch / train_data_size))\n",
    "    print(\"------\")\n",
    "    train_loss_list.append(total_train_loss_pre_epoch / train_data_size)\n",
    "    train_accuracy_list.append(total_train_accuracy_pre_epoch / train_data_size)\n",
    "    \n",
    "    writer.add_scalar(\"train_loss\", total_train_loss_pre_epoch / train_data_size, i)\n",
    "    writer.add_scalar(\"train_acc\", total_train_accuracy_pre_epoch / train_data_size, i)\n",
    "    \n",
    "    \n",
    "    mymod.eval()\n",
    "    total_test_loss_pre_epoch = 0\n",
    "    total_test_accuracy_pre_epoch = 0\n",
    "    # 去掉梯度\n",
    "    with torch.no_grad():\n",
    "        for data in test_dataloader:\n",
    "            imgs, targets = data\n",
    "            imgs = imgs.to(device)\n",
    "            targets = targets.to(device)\n",
    "            outputs = mymod(imgs)\n",
    "\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            total_test_loss_pre_epoch += loss.item() * targets.size(0)\n",
    "            # 统计在测试集上正确的个数，然后累加起来\n",
    "            accuracy = (outputs.argmax(1) == targets).sum()\n",
    "            total_test_accuracy_pre_epoch += accuracy.item()\n",
    "\n",
    "\n",
    "    end_test_time = time.time()\n",
    "    print(\"第{}轮测试用时：{} s\".format(i, end_test_time - end_train_time))\n",
    "    print(\"第{}轮测试loss：{}\".format(i, total_test_loss_pre_epoch / test_data_size))\n",
    "    print(\"第{}轮测试精度：{}\".format(i, total_test_accuracy_pre_epoch / test_data_size))\n",
    "    print(\"------\")\n",
    "    test_loss_list.append(total_test_loss_pre_epoch / test_data_size)\n",
    "    test_accuracy_list.append(total_test_accuracy_pre_epoch / test_data_size)\n",
    "    \n",
    "    writer.add_scalar(\"test_loss\", total_test_loss_pre_epoch / test_data_size, i)\n",
    "    writer.add_scalar(\"test_acc\", total_test_accuracy_pre_epoch / test_data_size, i)\n",
    "\n",
    "\n",
    "torch.save(mymod, \"./{}.pth\".format(save_model_name))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cab279b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T10:22:54.451701Z",
     "iopub.status.busy": "2024-05-12T10:22:54.451385Z",
     "iopub.status.idle": "2024-05-12T10:22:56.188145Z",
     "shell.execute_reply": "2024-05-12T10:22:56.187146Z"
    },
    "papermill": {
     "duration": 1.755201,
     "end_time": "2024-05-12T10:22:56.190240",
     "exception": false,
     "start_time": "2024-05-12T10:22:54.435039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plt.plot(x,y)\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_loss_list)\n",
    "plt.title(\"train_loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, test_loss_list)\n",
    "plt.title(\"test_loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_accuracy_list)\n",
    "plt.title(\"train_accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, test_accuracy_list)\n",
    "plt.title(\"test_accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_loss_list, label=\"train\")\n",
    "plt.plot(epoch_step, test_loss_list, label=\"test\")\n",
    "plt.legend()\n",
    "plt.title(\"loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_accuracy_list, label=\"train\")\n",
    "plt.plot(epoch_step, test_accuracy_list, label=\"test\")\n",
    "plt.legend()\n",
    "plt.title(\"accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "databundleVersionId": 31148,
     "sourceId": 3362,
     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30699,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 860.462285,
   "end_time": "2024-05-12T10:22:58.695481",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-05-12T10:08:38.233196",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
